1. Field of the Invention
The present invention concerns identifying regions of interest in medical images of a subject.
2. Description of the Prior Art
The definition of regions or volumes of interest (ROI/VOI) is a typical precursor to quantitative analysis of medical images, such as nuclear medicine emission images (for example, PET or SPECT). Such regions may be defined around areas of high intensity which correspond to high tracer uptake (hotspots). For example, in FDG-PET images for oncology, such areas may be indicative of the presence of a tumor. Oncology physicians frequently annotate lesions in PET scans for the purpose of making a diagnosis, or for use in radiotherapy. The mean or maximum tracer uptake can aid a reader in determining the likelihood of cancer. In longitudinal studies, considering the change in intensity or uptake on corresponding VOIs from images at different temporal stages may be used to determine whether a tumor has regressed.
The ROI/VOI delineation step is generally a user interactive process. In PET, it is common to define such regions using a manually adjusted threshold either defined on an absolute scale or with reference to a local maximum in intensity, or some other reference region.
The issue in such threshold based segmentations is the adjustment of the threshold. It should be adjusted such that the object of interest is included in the VOI but such that the background is not. In some cases this adjustment is made difficult by the presence of other high uptake structures or features of the image adjacent to the region of interest. For example a lung tumor may be close to the heart left ventricle, a site of typical high uptake in FDG-PET. Alternatively, there may be several tumors in close proximity to one another and the user may wish to delineate each separately.
The difficulty is more pronounced in 3D than in 2D since the user must check each slice over which the VOI is defined, since connectivity between voxels included in the object of interest may be present across voxels not in the current displayed slices. This can be slow and laborious.
Previously considered methods of hotspot identification or lesion annotation include the following:
Local Threshold & Connected Component Method: a containing region, surrounding a particular hotspot, is selected and a threshold is chosen to select those voxels within the region corresponding to the lesion. The threshold is used to apply a Boolean inclusion criterion to each voxel within the region and the locations of the included voxels are stored in an annotated region. Optionally, a final step to label islands of included voxels individually may be applied, all or some of which may be accepted by the user as valid annotated regions. The problems with this approach are:                the speed of the algorithm depends on the size of the containing region, and can be too slow for real-time feedback for larger regions        if the original containing region excluded part of the annotated region (for example on a different slice of the volume) it will need to be redrawn and the process repeated        multiple user interactions (e.g. mouse clicks) are required (for example, to create the containing region and update the threshold value)        
Watershed method: a point is selected and expanded until an intensity threshold is reached, defining an annotated region. The threshold is used as a termination criterion of, for example, a watershed type algorithm. Although this method has a simpler user interface, it is also problematic since very permissive thresholds could potentially select most of the voxels within the image making the algorithm slow. The slowness occurs because the algorithm's speed is dependent on the number of voxels included in the final ROI. Two separate actions are required of the user: selecting the initial seed-point, then updating the threshold until the annotated region is acceptable (until it has segmented a lesion correctly).
Global Threshold & Connected Component Method: a global threshold may be applied to the image, where only voxels above the threshold are included. The sub-region surrounding a lesion is then selected. This algorithm can be slow and requires several inputs from the user. This is essentially the same as the Local Threshold and Connected Component Method, except the containing region is the entire image.
Manually segmenting out a region in the image: this can be very time-consuming if there are many lesions to be annotated or if the lesion covers multiple slices.
Automatic systems for selecting VOIs exist but these typically generate spurious regions which must be rejected. Those remaining typically require individual manual adjustment as well.
Many of these methods may be launched from a determined bounding region in the image data. To prevent the inclusion of extraneous structures that are within close proximity to an ROI/VOI, a user can manually define a sufficiently small bounding region that takes the form of a box or an ellipse. The threshold operation is defined only within this outer region. This works in some cases but is difficult and sometimes impossible in others. For example, in some cases this may not be possible, as shown for a 2D example in FIG. 1, where a box is used to define the bounding region.
FIG. 1 is a diagram showing a cropped image taken from a single PET slice. In the image there are two maxima 102, 104 indicated by the two crosses. The region of interest 101 to the user is indicated by the solid freeform curve 106 in the image, and the bounding region is indicated by the dashed rectangle 108. An additional region 103 that is included by the box is indicated by the dotted freeform curve (110).
The region the user is interested in (the ROI 101) is shown as a solid freeform curve. If the box 108 in FIG. 1 is made too small, then part of the ROI will be excluded. Alternatively, if the box is too large then parts of another region 103 will be included within the box. In 3D, only one slice may be in view at a time, so the user may not even be able to tell if extraneous structures have been included without inspecting each slice individually. One solution to this problem is to apply a connected component algorithm to separate the two regions within the bounding region. The user may then indicate which potential ROI should be rejected. Every time the threshold is changed, the connected component algorithm needs to be re-applied.
However, this solution will be effective only if the user selects a threshold such that the two structures are indeed spatially disconnected. This requires that the user adjusts the threshold and then inspects the resulting segmentation to determine if the desired result has been obtained.
Alternatively, the user may define a bounding region by delineating a set of free form curves in 2D: one curve for each slice of the scan. This can be a time consuming process. In addition, the changes in shades of intensity may be too subtle for the user to tell if extraneous nearby structures have inadvertently been included within the delineated bounding region.